Relationship Between Perioperative Medication and Prolonged Postoperative Hospital Stay in Older Adults with Spinal Surgery: a Retrospective Cohort Study
Bibliographic record
Abstract
Background: Older people are prone to multiple chronic diseases and, as a result, require multiple medications. At present, there is no study to verify whether the use of high-risk perioperative medications (HRPOMs) will adversely affect postoperative outcomes in the relatively old patient. In this study, we aimed to analyze the risks of HRPOMs for prolonged length of hospital stay (LOS) in advanced-aged (≥ 75 years) patients undergoing spinal surgery. Methods: Medical records of advanced-aged patients who underwent spinal surgeries were retrospectively reviewed. Patients were divided into those who had prolonged LOS (≥ eight days) versus those who did not (< eight days). The demographics, medical comorbidities, and perioperative medications were analyzed. Univariate and multivariate regression were used to determine perioperative risk factors for prolonged LOS. Results: A total of 268 patients were included with a median age of 79 years (interquartile range [IQR]=76, 82) and 127 (47.4%) patients had a prolonged LOS. In multivariate logistic analysis, higher body mass index (odds ratio [OR] = 1.116; 95% CI, 1.031-1.209), operation time (OR) = 1.009; 95% CI, 1.005-1.012), and number of postoperative HRPOMs (OR= 1.910; 95% CI, 1.464-2.492) were identified as independent predictors for prolonged LOS. The use of metformin was associated with lower likelihood of prolonged LOS in diabetic patients (OR = 0.365; 95% CI, 0.157-0.846). Conclusion: Our results indicate that the higher number of postoperative HRPOMs, rather than a specific HRPOMs type, is a risk factor for prolonged LOS. The continued preoperative use of metformin in patients with diabetes has a positive impact on the postoperative outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".